Methods and systems for determining household characteristics

ABSTRACT

A system and method for recommending products based on characteristics of a customer&#39;s household. The system and method associates age dependent products with developmental stages on a universal developmental scale and determines a subset of age dependent products based on prior engagements by the customer&#39;s household. Using the development stages associated with the subset of age dependent products characteristics of the customer&#39;s household may determine specifically the number and ages of juveniles in the customer&#39;s household. Performing Gaussian mixture model or multivariate kernel density estimation on the developmental stages associated with the engagements of customer&#39;s household, the age(s) and number of juveniles respectively may be determined and recommendations of products and services to the customer or customer&#39;s household based upon these characteristics may be advantageously made.

TECHNICAL FIELD

The disclosed subject matter relates generally to the determination ofcharacteristics of a consumer's household in order gain an understandingof the household and thus provide tailored recommendations of productsand services. Specifically, the disclosed subject matter determines thenumber and ages of children in a customer's family, in order to enhancethe customer's shopping experience.

BACKGROUND

Current technology related to on-line shopping platforms cannot identifycharacteristics of the customer's household related to number and agesof the juvenile members. This inability can limit the recommendationsand effectiveness of marketing directed to the customer.

Additionally, the diversity and applicability of scales in whichproducts and services are associated with developmental stages ofjuveniles, further hampers the recommendations and effectiveness ofmarketing directed to the customer. Understanding age related productsis important to helping parents throughout their shopping experienceduring the stages of a child's development, unfortunately children'sproducts such as diapers and baby food have disparate age identifyingattributes. Different products have different attributes which may beassociated with the age (or developmental stage, or size associated withan age range) as illustrated in Table 1.

TABLE 1 Product Attributes Value (e.g. age, Product Attributesdevelopmental stage) Diaper size size1, size2, size3, size4 . . .Clothes baby_clothing_size Preemie, Newborn, 3-6 Months, 3T, 4T, 2-3 yrsFoods baby_food_stage stage 1, stage 2, stage 3 . . . Toys age_range 2-4Months, 1-2 yrs., 5+, 12 up

In order to effectively make marketing decisions and recommendations,the retailer needs to understand the correspondences between thesedifferent attributes and the respective overlaps, in addition toknowledge of the age or developmental stage of a juvenile householdmember. Furthermore, an attempt to ascertain these characteristics ofthe household based upon product engagements becomes more fraught withuncertainties because of these disparate scales and attributes.

The disclosed subject matter addresses these problems by firstestablishing a universal scale that distinguishes early developmentalstages of children, and then identifies these attributes of the productsand services, and translates them into the correct range on a universalscale. The products and services are associated with the age ordevelopmental stage. Further, leveraging the association with theuniversal scale, the disclosed subject matter, utilizing the customerhousehold's engagements with product or services that are associatedwith age or developmental stages on the universal scale may be analyzedusing a classic methodology of Gaussian Mixture Model to predict theage(s) of juvenile members of a customer's household. An evaluationmetric is based on statistical reasoning validates the performance ofthe model.

Placing the customer's children in the appropriate developmental stageaids in the understanding of the parent's particular shopping needsenables dynamic understanding of children's age(s) and further enablescontinuing information regarding the development of juveniles in thehousehold allowing the retailer to provide better shopping guidance forcustomers throughout their parenting journey. Thus correct ageidentification aids with ads targeting, recommendations, and customerrelationship management (CRM). Moreover, as parents need to purchase alot of products with the appropriate age attribute (e.g. a particularsize for diapers) especially during early stages of children'sdevelopment, correctly predicting children's age(s) and anticipatingsuch needs, creates a smooth and personalized shopping journey forparents along with increased revenue potential, and customer loyalty.

Similarly, knowing the number of children in a customer's household isalso an important part of understanding one's parenting journey.Similarly, knowing the number of children enables the identification anddistinguishing between different shopping journeys for one customer, andfurther enables better shopping guidance for customers throughout theirparenting journey. Without a prior understanding the number of children,effective selection of recommendations, properly tailored marketing anddispensing of appropriate parent guidance can be hindered by otherwiseseemingly erratic purchasing behavior. With a correctly predicted numberof children in a customer's household, the retailer may build a focusedjourney around each child, generating more sales and customersatisfaction.

The disclosed subject matter to address these issues similarly leveragesthe association of products and services with the universal scale, andthe prior engagements of customer's household with those product orservices to determine the number of children in the customer'shousehold. The engagements may be analyzed using a classic methodologyof multivariate kernel density estimation in predicting the number ofchildren in a customer's household or associated with a customer.

SUMMARY

The embodiments described herein are directed to systems and methods fordetermining household characteristics based at least in part on pasthousehold engagements with the retailer In addition to or instead of theadvantages presented herein, persons of ordinary skill in the art wouldrecognize and appreciate other advantages as well.

In accordance with various embodiments, exemplary systems may beimplemented in any suitable hardware or hardware and software, such asin any suitable computing device.

In some embodiments, a system for recommending products based oncharacteristics of a customer's household. The system including acomputing device connected to a database via a communication system, thecomputing device associating age dependent products in the database withdevelopmental stages on a universal developmental scale; determining, asubset of age dependent products based on prior engagements by thecustomer household; and retrieving, from the database, the developmentstages associated with the subset of age dependent products. Thecomputing device also performing Gaussian Mixture modeling upon theretrieved development stages, and from the results of the GaussianMixture modeling, determining a developmental stage (i.e. age(s))associated with the customer household; and, recommending selective onesof age dependent products to the customer's household based upon thedetermined developmental stage.

In other embodiments, a method for recommending products based oncharacteristics of a customer's household is provided. The methodincluding associating a plurality of age dependent products with adevelopmental stage on a universal developmental scale, where theuniversal developmental scale includes of a plurality of sequentialdevelopmental stages; determining, a subset of age dependent productsbased on engagements by the customer household; and retrieving thedevelopment stages associated with each of determined subset of agedependent products in the subset. The method further includingperforming a Gaussian mixture modeling upon the retrieved developmentstages, determining a developmental stage associated with the customer'shousehold based on results from the Gaussian mixture model; and,recommending selective ones of age dependent products to the customerhousehold based upon the determined developmental stage.

In yet other embodiments, a non-transitory computer readable mediumhaving instructions stored thereon is provided. The instructions, whenexecuted by at least one processor, cause a device to perform operationsincluding associating age dependent products with a developmental stageon a universal developmental scale; determining; a subset of agedependent products based on engagements by the customer household; andretrieving the development stages associated with the subset of agedependent products. The operations further including performing Gaussianmixture modeling upon the retrieved development stages, determining adevelopmental stage associated with the customer household based onresults from the Gaussian mixture model; and, recommending age dependentproducts to the customer household based upon the determineddevelopmental stage.

In additional embodiments, a system for recommending products based oncharacteristics of a customer's household. The system including acomputing device connected to a database via a communication system, thecomputing device associating a age dependent products in the databasewith developmental stages on a universal developmental scale;determining, a subset of age dependent products based on priorengagements by the customer's household; and retrieving, from thedatabase, the development stages associated with the subset of agedependent products. The computing device also performing a multivariatekernel density estimation upon the retrieved development stages, andfrom the results of the estimation, determining a number of juveniles(i.e. number of developmental stages) associated with the customer'shousehold; and, recommending products to the customer's household basedupon the number of juveniles.

In still other embodiments, a method for recommending products based oncharacteristics of a customer's household is provided. The methodincluding associating a plurality of age dependent products with adevelopmental stage on a universal developmental scale, where theuniversal developmental scale includes of a plurality of sequentialdevelopmental stages; determining, a subset of age dependent productsbased on engagements by the customer's household; and retrieving thedevelopment stages associated with each of determined subset of agedependent products in the subset. The method further includingperforming a multivariate kernel density estimation upon the retrieveddevelopment stages, determining a number of juveniles associated withthe customer's household based on results from the estimation; and,recommending products to the customer's household based upon thedetermined developmental stage.

In further embodiments, a non-transitory computer readable medium havinginstructions stored thereon is provided. The instructions, when executedby at least one processor, cause a device to perform operationsincluding associating age dependent products with a developmental stageon a universal developmental scale; determining; a subset of agedependent products based on engagements by the customer's household; andretrieving the development stages associated with the subset of agedependent products. The operations further including performing amultivariate kernel density estimation upon the retrieved developmentstages, determining a number of juveniles associated with the customer'shousehold based on results from the estimations; and, recommendingproducts to the customer's household based upon the number of juveniles.

In yet further embodiments, a system for reconciling product attributescales to a universal scale. The system including a computing deviceconnected to a database via a communication system, the computing devicecorrelating a first scale and a second scale with a universaldevelopmental scale; associating age dependent products in the databasewith one or more developmental stages on the universal developmentalscale based upon the correlation; and, transmitting each of theassociated one or more developmental stages to the database over thecommunication system for storage with the respective age dependentproduct in the database; wherein some of the age dependent products areassociated with the first scale and others of the are associated withthe second scale which is different from the first scale.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and advantages of the present disclosures will be morefully disclosed in, or rendered obvious by the following detaileddescriptions of example embodiments. The detailed descriptions of theexample embodiments are to be considered together with the accompanyingdrawings wherein like numbers refer to like parts and further wherein:

FIG. 1 is a block diagram of communication network for determininghousehold characteristics in accordance with some embodiments;

FIG. 2 is a block diagram of the household characteristic determiningcomputing device of the communication system of FIG. 1 in accordancewith some embodiments;

FIG. 3 is a schematic diagram representing the correspondence of auniversal scale with disparate scales in accordance with embodiments ofthe disclosed subject matter;

FIG. 4 is a diagram of operations of the household characteristicdetermining computing device in accordance with embodiments of thedisclosed subject matter;

FIG. 5 is a flowchart of a method for determining a householdcharacteristic, specifically, the age of juveniles in the customer'shousehold in accordance with embodiments of the disclosed subjectmatter;

FIG. 6 is a flowchart of a method for determining a householdcharacteristic, specifically, the number of juveniles in the customer'shousehold in accordance with embodiments of the disclosed subjectmatter; and,

FIG. 7 is a flowchart of a method for providing product recommendationsto a customer based on the household characteristics in accordance withembodiments of the disclosed subject matter.

The description of the preferred embodiments is intended to be read inconnection with the accompanying drawings, which are to be consideredpart of the entire written description of these disclosures. While thepresent disclosure is susceptible to various modifications andalternative forms, specific embodiments are shown by way of example inthe drawings and will be described in detail herein. The objectives andadvantages of the claimed subject matter will become more apparent fromthe following detailed description of these exemplary embodiments inconnection with the accompanying drawings.

DETAILED DESCRIPTION

It should be understood, however, that the present disclosure is notintended to be limited to the particular forms disclosed. Rather, thepresent disclosure covers all modifications, equivalents, andalternatives that fall within the spirit and scope of these exemplaryembodiments. The terms “couple,” “coupled,” “operatively coupled,”“operatively connected,” and the like should be broadly understood torefer to connecting devices or components together either mechanically,electrically, wired, wirelessly, or otherwise, such that the connectionallows the pertinent devices or components to operate (e.g.,communicate) with each other as intended by virtue of that relationship.

Turning to the drawings, FIG. 1 illustrates a block diagram of acommunication system 100 that includes a household characteristicdetermining computing device 102 (e.g., a server, such as an applicationserver), a web server 104, databases 116 and 117, and multiple customercomputing devices 110, 112, 114 operatively coupled over network 118.

A household characteristic determining computing device 102, server 104,and multiple customer computing devices 110, 112, 114 can each be anysuitable computing device that includes any hardware or hardware andsoftware combination for processing and handling information. Forexample, each can include one or more processors, one or morefield-programmable gate arrays (FPGAs), one or more application-specificintegrated circuits (ASICs), one or more state machines, digitalcircuitry, or any other suitable circuitry. In addition, each cantransmit data to, and receive data from, or through the communicationnetwork 118.

In some examples, the household characteristic computing device 102 canbe a computer, a workstation, a laptop, a server such as a cloud-basedserver, or any other suitable device. In some examples, each of multiplecustomer computing devices 110, 112, 114 can be a cellular phone, asmart phone, a tablet, a personal assistant device, a voice assistantdevice, a digital assistant, a laptop, a computer, or any other suitabledevice. In some examples, intent-free answering computing device 102,and web server 104 are operated by a retailer, and multiple customercomputing devices 112, 114 are operated by customers of the retailer.

Although FIG. 1 illustrates three customer computing devices 110, 112,114, communication system 100 can include any number of customercomputing devices 110, 112, 114. Similarly, the communication system 100can include any number of workstation(s) (not shown), intent freeanswering computing devices 102, web servers 104, and databases 116 and117.

The household characteristic determining computing device 102 isoperable to communicate with databases 116 over communication network118. For example, household characteristic determining computing device102 can store data to, and read data from, databases 116 and 117.Databases 116 may be remote storage devices, such as a cloud-basedserver, a disk (e.g., a hard disk), a memory device on anotherapplication server, a networked computer, or any other suitable remotestorage. Although shown remote to the household characteristicdetermining computing device 102, in some examples, databases 116 and117 may be a local storage device, such as a hard drive, a non-volatilememory, or an USB stick. The household characteristic determiningcomputing device 102 may store data from workstations or the web server104 in database 116. In some examples, storage devices storeinstructions that, when executed by household characteristic determiningcomputing device 102, allow intent free answering computing device 102to determine one or more s results in response to a user query.

Communication network 118 can be a WiFi x network, a cellular networksuch as a 3GPP® network, a Bluetooth® network, a satellite network, awireless local area network (LAN), a network utilizing radio-frequency(RF) communication protocols, a Near Field Communication (NFC) network,a wireless Metropolitan Area Network (MAN) connecting multiple wirelessLANs, a wide area network (WAN), or any other suitable network.Communication network 118 can provide access to, for example, theInternet.

FIG. 2 illustrates the household characteristic determining computingdevice 102 of FIG. 1 . Household characteristic determining computingdevice 102 may include one or more processors 201, working memory 202,one or more input/output devices 203, instruction memory 207, atransceiver 204, one or more communication ports 207, and a display 206,all operatively coupled to one or more data buses 208. Data buses 208allow for communication among the various devices. Data buses 208 caninclude wired, or wireless, communication channels.

Processors 201 can include one or more distinct processors, each havingone or more processing cores. Each of the distinct processors can havethe same or different structure. Processors 201 can include one or morecentral processing units (CPUs), one or more graphics processing units(GPUs), application specific integrated circuits (ASICs), digital signalprocessors (DSPs), and the like.

Processors 201 can be configured to perform a certain function oroperation by executing code, stored on instruction memory 207, embodyingthe function or operation. For example, processors 201 can be configuredto perform one or more of any function, method, or operation disclosedherein.

Instruction memory 207 can store instructions that can be accessed(e.g., read) and executed by processors 201. For example, instructionmemory 207 can be a non-transitory, computer-readable storage mediumsuch as a read-only memory (ROM), an electrically erasable programmableread-only memory (EEPROM), flash memory, a removable disk, CD-ROM, anynon-volatile memory, or any other suitable memory.

Processors 201 can store data to, and read data from, working memory202. For example, processors 201 can store a working set of instructionsto working memory 202, such as instructions loaded from instructionmemory 207. Processors 201 can also use working memory 202 to storedynamic data created during the operation of intent free answeringcomputing device 102. Working memory 202 can be a random access memory(RAM) such as a static random access memory (SRAM) or dynamic randomaccess memory (DRAM), or any other suitable memory.

Input-output devices 203 can include any suitable device that allows fordata input or output. For example, input-output devices 203 can includeone or more of a keyboard, a touchpad, a mouse, a stylus, a touchscreen,a physical button, a speaker, a microphone, or any other suitable inputor output device.

Communication port(s) 209 can include, for example, a serial port suchas a universal asynchronous receiver/transmitter (UART) connection, aUniversal Serial Bus (USB) connection, or any other suitablecommunication port or connection. In some examples, communicationport(s) 209 allows for the programming of executable instructions ininstruction memory 207. In some examples, communication port(s) 209allow for the transfer (e.g., uploading or downloading) of data, such asmachine learning algorithm training data.

Display 206 can display user interface 205. User interfaces 205 canenable user interaction with household characteristic determiningcomputing device 102. In some examples, a user can interact with userinterface 205 by engaging input-output devices 203. In some examples,display 206 can be a touchscreen, where user interface 205 is displayedby the touchscreen.

Transceiver 204 allows for communication with a network, such as thecommunication network 118 of FIG. 1 . For example, if communicationnetwork 118 of FIG. 1 is a cellular network, transceiver 204 isconfigured to allow communications with the cellular network. In someexamples, transceiver 204 is selected based on the type of communicationnetwork 118 household characteristic determining computing device 102will be operating in. Processor(s) 201 is operable to receive data from,or send data to, a network, such as communication network 118 of FIG. 1, via transceiver 204.

FIG. 3 illustrates a schematic diagram of the creation of a universalscale in which to associated products and services with developmentalstages of children. Creation of a universal scale is a precursor to thehousehold characteristic modelling system and method generallyillustrated in FIG. 4 .

Referring back to FIG. 3 , as described previously, age labels fromdifferent product types are required to be translated into a singlelabeling schema to distinguish developmental stages of children fromnewborn to pre-teen. The universal scale 300 is shown in ten incrementsfrom 301-310. These increments may be representative of years, ormonths, or factions/multiples thereof, they may be constant valueincrements, or of varying size, any number of increments may be used tocover the desired developmental stages. More increments generally allowfor greater granularity. For example, during the first year in may beadvantageous to have greater granularity such that each quarter of theyear is associated with a different universal scale value, whereaspreteens between the ages of 10 and 13 may be represented by only onevalue on the universal scale. Three product scales are illustrated inFIG. 3 corresponding to the development stages ranging from 321-326. Thefirst product scale 330 may be for example clothes or shoes in whichmultiple sizes 331, 333, 335, 337 and 339 span the developmental stages.The second product scale 340 may be for toys, such that the same toyscale value 342, 344 or 346 product value may be associated withmultiple developmental stages, and may also overlap with other scalevalues, for example the end of 342 overlaps the scale value 344. Thethird scale 350 may be representative of accessories, such as babybottles 351, sippy cups 352, etc., in which the accessory is normallyused over one or more development stages and then a different product isused, i.e. a child moves from a bottle to a sippy cup with littleoverlap. The different scales 330, 340, 350 may also be representativeof the same type of product but from different suppliers using differentscales.

In the universal scale 300, the products and product scales,irrespective of the relative scale or attribute used to describe theappropriate targeted developmental stage, may be associated with one ormore values on the universal scale 300. Thus products associated withvalue 333 on scale 330, value 344 on scale 340, or value 352 on scale350 would all be appropriately associated with and reflective of a childassociated with value “4” 304 on the universal scale. Thus, as describedfurther below, a customer selecting products with respective values of333, 344 and 352 could be assumed to have a household with a child indevelopmental stage 323 or 324 associated respectively with universalscale “3” 303 or “4” 304. This association over multiple products andservices as describe in the present subject matter may narrow down thedevelopmental stage based on the universal scale associated with theproducts.

In one embodiment, the universal scale may be reflective of ages ofpre-determined segments {‘0 1’, ‘1 2’, ‘2 4’, ‘4 7’, ‘7 13’} in otherembodiments the pre-determined segments may be {0-3 months, 3 months-6months, 6 months-1 year, 1 yr.-2 yrs., 2 yrs.-3 yrs., 3 yrs.-4 yrs. 4yrs.-6 yrs., 6 yrs.-9 yrs. and 9 yrs.-13 yrs.}

In translating to a universal scale, there may be some noise in the itemcatalog and/or attribute values may be missing and thus dataextrapolation using various item attributes may be used to associate theitem/product with a value on the universal scale. Furthermore, therespective values on the universal scale may be estimated for newproducts or product types without labels based on other productspreviously associated with the universal scale, using item attributesfrom catalog, co bought behavior among items, text processing andmatching and extrapolation using matrix factorization. For exampleproducts with unknown values/attributes having transaction, views, andadd to cart and other engagement behavior corresponding with otherproducts having a universal scale value, the products with unknownvalue/attributes may be assigned a universal value equal to thecorresponding products.

In accordance with the disclosed subject matter, each product or servicetargeted towards juveniles are associated with the universal scale valuein the retailer's database 116. It is envisioned that similar universalscales may be associated with other household characteristics other thanthose related to juveniles. For example, the disclosed subject mattermay be used to estimate education level of individuals in a householdfor e.g. college levels (freshmen, sophomore etc.) or any otherage-based scale.

FIG. 4 illustrates of the operations of the household characteristicdetermining computing device 102. Accessing the retailer's catalog 410,the computing device 102 associates the products with a developmentalstage/value on the universal development scale in universal scalingmodule 402 a and then stores the value with the respective product insegment 416 a of database 116. As noted above, various methods may beundertaken including accessing past engagement data stored in thedatabase 116 in order to ensure each age dependent product related tojuveniles has an associated universal value.

The computing device 102 further accesses the engagements of eachparticular customer's household with the age/stage dependent productsstored in database segment 416 b and retrieves the associateddevelopment value (universal value) stored in the database 416 a. Withthe development values from the customer's household engagements, thecomputing device 102 in the characterization modeling module 402 bperforms Gaussian mixture modeling (where the characteristic desired injuvenile age) to determine the probability the household containing achild at one or more of the developmental stages. A Gaussian mixturemodel is a probabilistic model that assumes all the data points aregenerated from a mixture of a finite number of Gaussian distributionswith unknown parameters.

The results of the Gaussian mixture modeling (e.g. likely universalscale value) is then associated with the customer's household in thedatabase 416 b. The computing device 102, may further update the storeduniversal scale value associated with the customer household as timeelapses on a periodic basis such as monthly or yearly.

In subsequent interactions by the customer's household 401 with theretailer 420, specifically online shopping website, app or in-storecommunications, the computing device via the product selection module402 c retrieves the likely universal scale value representing theage/developmental stage from the database segment 416 b, accessesproduct database 416 a, and recommends selective products correspondingto the likely universal scale value (age of the juvenile). Therecommendations may be in the form of presenting images of the selectiveproducts to the customer or other member associated with the customer'shousehold via a website, applications, marketing ads via emails, textmessages, mail, or social media as well as other vehicles amenable topersonalized marketing.

Similarly, the household characteristic determined by thecharacterization module 402 b may be the number of juveniles in thehousehold. In determining the number of juveniles in the household, thecomputing device 102 accesses the engagements of each particularcustomer's household with the age/stage dependent products stored indatabase segment 416 b and retrieves the associated development value(universal value) stored in the database 416 a. With the developmentvalues from the customer's household engagements, the computing device102 in the characterization modeling module 402 b, performs amultivariate kernel density estimation to determine the number ofjuveniles in the household. Kernel density estimation is the process ofestimating an unknown probability density function using a kernelfunction. Unlike a histogram that counts the number of data points insomewhat arbitrary regions, a kernel density estimate is a functiondefined as the sum of a kernel function on every data points. The goalof density estimation is to take a finite sample of data and to makeinferences about the underlying probability density function everywhere,including where no data are observed. In kernel density estimation, thecontribution of each data point is smoothed out from a single point intoa region of space surrounding it. Aggregating the individually smoothedcontributions gives an overall picture of the structure of the data andits density function. The estimation resolves the data into nodes,representing distinct developmental stages (i.e. separate children).

The results of the kernel density estimation (number of juveniles) isthen associated with the customer's household in the database 416 b. Thecomputing device 102, may further update the number of juvenilesassociated with the customer's household as time elapses on a periodicbasis such as monthly or yearly.

FIG. 5 is a flow diagram for a method of determining the age ofjuveniles in the customer's household. Prior to determining the age ofjuveniles in the customers household, each of a plurality of agedependent products are associated with a universal development scale asshown in Block 501. A subset of age dependent products which have beenengaged by the customer's household is determined from the plurality ofage dependent products, as shown in Block 503. The subset may bedetermined from the historical engagements with the products store inthe database 116 as discussed previously. The universal developmentstages associated with each of the products in the subset are retrievedin Block 505 and Gaussian mixture modeling is performed upon theretrieved universal development stages as shown in Block 507. From theGaussian mixture modeling, a universal development stage is associatedwith the customer's household as shown in Block 509. Using at least theuniversal development stage associated with the customer's household, aplurality of age dependent products may be selected and recommended tothe customer's household via a website, app, or other personalizedmarketing channel as shown in Block 511.

FIG. 6 is a flow diagram for a method of determining the number ofjuveniles in the customer's household. As noted earlier embodiments ofthe described method require products to be associated with adevelopment stage and preferably as described herein to a universaldevelopment stage, as shown in Block 601. A subset of age dependentproducts which have been engaged by the customer's household isdetermined from the plurality of age dependent products, as shown inBlock 603. The subset may be determined from the historical engagementswith the products store in the database 116 as discussed previously. Theuniversal development stages associated with each of the products in thesubset are retrieved in Block 605 and unlike the previous method inwhich the age of the juveniles associated with the customer's householdis determined, a multivariate kernel density estimate is performed uponthe retrieved universal development stages as shown in Block 607. Fromthe multivariate kernel density estimate, a number of juveniles in thehousehold may be determined and is associated with the customer'shousehold in database 116 as shown in Block 609. Using at least thenumber of juveniles associated with the customer's household, aplurality of products are selected and recommended to the customer'shousehold via a website, app, or other personalized marketing channel asshown in Block 611. Unlike, the age determining method described in FIG.5 , the number of juveniles in the household may influence and beassociated with products irrespective of whether the products/servicesare associated with a developmental stage, for example a customer'shousehold with a large number of children may be offered bulk sizes ofproducts, or less expensive product, whereas a household with a smallernumber of juveniles may be offered via recommendation smaller quantitypackaging or more expensive products. The ability to recommend productsand services may be further enhanced using both the number of juvenilesand their respective ages.

FIG. 7 is a flow diagram for a method 700 of providing productrecommendations to a customer. A target customer household is firstidentified, as shown in Block 701, the identification may in someembodiments result from an interaction by the customer with theretailer, for example accessing the website, opening a mobileapplication, or entering a store etc. The customer's household may beidentifiable by more than one customer, for example both a mother andfather may be associated with the same household, similarly other adultsaunts, uncles and grandparents may also be associated with thecustomer's household given a sufficient amount of purchasing behaviorwith respect to the household's juvenile(s). Conversely each adultcustomer may be associated with a unique household even if the juvenilesare common for each household.

Once the customer's household has been identified, the characteristicsof the household are retrieved by the computing device 102 from thedatabase 116 as shown in Block 703. In embodiments discussed herein, thecharacteristics retrieved may include the number and/or ages of thejuveniles within the household. Using these characteristics thecomputing device 102 may select specific products/services targeted tothe number and/or ages of the juveniles, as shown in Block 705. Theselected products are then presented to the customer as shown in Block707. An advantage the understanding of the household characteristicsallows is that rather than displaying different sizes or age ranges, thecomputing device 102 may recommend only those product servicesappropriate to the age (as reflected in the universal scale) or numberof juveniles. For example in response to a customer's search query fordiapers instead of showing different diaper sizes and brands, theproduct selected may be personalized such that the diapers recommendedirrespective of brand/type would be of the appropriate size consistentwith the universal scale value associated with the customer's household.Similarly, different quantities may be recommended to the customer orcustomer's household based on the number of juveniles in the household,for example a family with multiple children may favor the selection ofbulk size packages of food, e.g. large or jumbo boxes of cereal, gallonof milk over smaller packaging sizes.

In recommending products/services to the customer, the retailer may rankproducts and services according to their correspondence to the householdcharacteristics, such that they are more likely to appear in a carouselof products, or in search results presented to the customer.Additionally the determined household characteristics may be used todirect personalized marketing campaigns in and outside of electronicmedia, such as personalized campaigns to families with kids, for e.g.:Back to School, Kid's Fashion etc.

The results of determining the household characteristics with a Gaussianmixture model and subsequent recommendations in testing were favorablewith respect to precision, recall, dot product and Jensen Shannondistance. Similar results were achieved with multivariate kernel densityestimation, for the estimation of the number of juveniles.

While the disclosed subject matter is particularly amenable todetermining the characteristic of a customer's household, where thecustomer is parent, it is similarly applicable to determining thecharacteristics of juveniles associated with a customer, such asbrothers, sisters aunts, uncles, grandparent, god parents, familyfriends and other relationships that result in more than a minimalamount of and continuing engagements with age dependent products andservices.

The terms juvenile(s), kid(s) and child/children are usedinterchangeable in describing the disclosed subject matter, nodistinction between these terms is intended.

Although the disclosed subject matter describes the characteristics ofthe household as age(s) and number of children in the household, otherhousehold characteristics are also envisioned as determinate via thesystem and method described herein.

Although the methods disclosed above are described with reference to theillustrated flowcharts, it will be appreciated that many other ways ofperforming the acts associated with the methods can be used. Forexample, the order of some operations may be changed, and some of theoperations described may be optional.

In addition, the methods and system described herein can be at leastpartially embodied in the form of computer-implemented processes andapparatus for practicing those processes. The disclosed methods may alsobe at least partially embodied in the form of tangible, non-transitorymachine-readable storage media encoded with computer program code. Forexample, the steps of the methods can be embodied in hardware, inexecutable instructions executed by a processor (e.g., software), or acombination of the two. The media may include, for example, RAMs, ROMs,CD-ROMs, DVD-ROMs, BD-ROMs, hard disk drives, flash memories, or anyother non-transitory machine-readable storage medium. When the computerprogram code is loaded into and executed by a computer, the computerbecomes an apparatus for practicing the method. The methods may also beat least partially embodied in the form of a computer into whichcomputer program code is loaded or executed, such that, the computerbecomes a special purpose computer for practicing the methods. Whenimplemented on a general-purpose processor, the computer program codesegments configure the processor to create specific logic circuits. Themethods may alternatively be at least partially embodied in applicationspecific integrated circuits for performing the methods.

The foregoing is provided for purposes of illustrating, explaining, anddescribing embodiments of these disclosures. Modifications andadaptations to these embodiments will be apparent to those skilled inthe art and may be made without departing from the scope or spirit ofthese disclosures.

What is claimed is:
 1. A system for recommending products based oncharacteristics of a customer's household, comprising: a computingdevice operably connected to a database via a communication system, thecomputing device configured to: correlate a first scale and a secondscale with a universal developmental scale; associate each of aplurality of age dependent products in the database with one or moredevelopmental stages on the universal developmental scale based upon thecorrelation; and, transmit each of the associated one or moredevelopmental stages to the database over the communication system forstorage with the respective age dependent product in the database;wherein ones of the plurality of age dependent products are associatedwith the first scale and others of the plurality of age dependentproducts are associated with the second scale, and wherein the secondscale is different from the first scale.
 2. The system of claim 1,wherein the computing device is further configured to: determine, fromthe plurality of age dependent products, a subset of age dependentproducts based on prior engagements by the customer's household;retrieve, from the database, the development stages associated with eachof the age dependent products in the subset; perform a multivariatekernel density estimation upon the retrieved development stages;determine a number of developmental stages associated with thecustomer's household based on results from the kernel densityestimation; and, recommend selective products to the customer'shousehold based upon the determined number of developmental stages. 3.The system of claim 2, wherein the database is configured such that thenumber of determined developmental stages is stored and associated as acharacteristic of the customer's household within the database.
 4. Amethod for recommending products based on characteristics of acustomer's household, comprising: determining, from a plurality of agedependent product, each having an associated developmental stage, asubset of age dependent products based on engagements by the customer'shousehold; retrieving a plurality of development stages associated witheach of the age dependent products in the subset; performing amultivariate kernel density estimation upon the retrieved developmentstages; determining a number of developmental stages associated with thecustomer's household based on results from the multivariate kerneldensity estimation; and, recommending products to the customer'shousehold based upon the determined number of developmental stages. 5.The method of claim 4, further comprising: associating each of theplurality of age dependent products with a respective developmentalstage on a universal developmental scale.
 6. The method of claim 5,wherein ones of the plurality of age dependent products are associatedwith a first scale.
 7. The method of claim 6, wherein others of theplurality of age dependent products are associated with a second scaledifferent from the first scale.
 8. The method of claim 7, wherein thestep of associating each of a plurality of age dependent products withthe universal developmental scale further comprises correlating thefirst and second scales with the universal developmental scale.
 9. Themethod of claim 4, wherein the each of the plurality of developmentalstages represents a time period.
 10. The method of claim 4, wherein theengagements by the customer's household are selected from the groupconsisting of purchases, add to cart, click on, queries, search result,and views.
 11. The method of claim 4, wherein the step of recommendingcomprises presenting images of the selective products on a website tothe customer.
 12. The method of claim 4, wherein the recommendedproducts have an attribute commensurate with the determined number ofdevelopmental stages.
 13. The method of claim 12, wherein the attributeis packaging size.
 14. A non-transitory computer readable medium havinginstructions stored thereon, wherein the instructions, when executed byat least one processor, cause a device to perform operations comprising:associating each of a plurality of age dependent products with adevelopmental stage on a universal developmental scale, the universaldevelopmental scale comprised of a plurality of sequential developmentalstages; determining, from the plurality of age dependent products, asubset of age dependent products based on engagements by the customer'shousehold; retrieving the development stages associated with each of theage dependent products in the subset; performing multivariate kerneldensity estimation upon the retrieved development stages; determining anumber of developmental stages associated with the customer's householdbased on results from the multivariate kernel density estimation; and,recommending products to the customer's household based upon thedetermined number of developmental stages.
 15. The non-transitorycomputer readable medium of claim 14, wherein ones of the plurality ofage dependent products are associated with a first scale.
 16. Thenon-transitory computer readable medium of claim 15, wherein others ofthe plurality of age dependent products are associated with a secondscale different from the first scale.
 17. The non-transitory computerreadable medium of claim 16, wherein the operation of associating eachof a plurality of age dependent products with the universaldevelopmental scale further comprises correlating the first and secondscales with the universal developmental scale.
 18. The non-transitorycomputer readable medium of claim 14, wherein the each of the pluralityof developmental stages represents a time period.
 19. The non-transitorycomputer readable medium of claim 14, wherein the engagements by thecustomer's household are selected from the group consisting ofpurchases, add to cart, click on, queries, search result, and views. 20.The non-transitory computer readable medium of claim 14, wherein theinstructions for the operation of recommending further comprisesinstructions for presenting images of the selected products on a websiteto the customer.